Energy management of a virtual power plant (VPP) that consists of wind farm (WF), energy storage systems and a demand response program is discussed in the present study. The introduced strategy is realized at the electrical power transmission level and takes into account the collaboration between VPPs in day‐ahead energy and reserve markets. One notable feature of the proposed strategy is attempting to make the revenue of VPPs close to the operating cost of generating units as much as possible. The objective function is subjected to the network‐constrained unit commitment model, up and down reserve requirements and the proposed VPP constraints. This method is taking into account the uncertainty in system and VPP loads, day‐ahead market energy and reserve price and WF power generation. This strategy is applied as hybrid stochastic‐robust scheduling to the VPP at the electrical power transmission level, where the scenario‐based stochastic programming models the uncertainty of day‐ahead market prices, and the bounded uncertainty‐based robust optimization has been adopted to model the uncertainties related to the load and WF power. Scheme has been tested on IEEE systems. According to the obtained results, the proposed coordination of VPPs in the mentioned markets demonstrates capability of suggested strategy.

This paper discusses the simultaneous management of active and reactive power of a flexible renewable energy-based virtual power plant placed in a smart distribution system, based on the economic, operational, and voltage security objectives of the distribution system operator. The formulated problem aims to specify the minimum weighted sum of energy cost, energy loss, and voltage security index, considering the optimal power flow model, voltage security formulation, and the operating model of the virtual power plant. The virtual unit includes renewable sources, like wind systems, photovoltaic, and bio-waste units. Flexibility resources include electric vehicle parking lot and price-based demand response. In the mentioned scheme, parameters of load, renewable sources, electric vehicles, and energy prices are uncertain. This paper utilizes the Unscented Transformation method for modeling uncertainties. Fuzzy decision-making is utilized to extract a compromised solution. The suggested approach innovatively considers the simultaneous management of active and reactive power of a virtual unit with electric vehicles and price-based demand response. This is performed to promote economic, operational, and network security objectives. According to numerical results, the approach with optimal power management of renewable virtual units is capable of boosting the economic, operation, and voltage security status of the network by approximately 43%, 47–62%, and 26.9%, respectively, to power flow studies. Only price-based demand response can improve the voltage security, operation, and economic states of the network by about 19.5%, 35–47%, and 44%, respectively, compared to the power flow model.

This paper presents a robust framework for active and reactive power management in distribution networks using electric vehicles (EVs). The method simultaneously minimizes the energy cost and the voltage deviation subject to network and EVs constraints. The uncertainties related to active and reactive loads, required energy to charge EV batteries, charge rate of batteries and charger capacity of EVs are modeled using deterministic uncertainty sets. First, based on duality theory, the max min form of the model is converted to a max form. Second, Benders decomposition is employed to solve the problem. The effectiveness of the proposed method is demonstrated with a 33-bus distribution network.